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Cited by 22Artificial Intelligence in EducationAssistive Technology in Communication and MobilityEducational Technology in Learning

Leveraging Education through Artificial Intelligence Virtual Assistance: A Case Study of Visually Impaired Learners

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Mina, Paul Niño Ry, Solon, Ice Mae, Sanchez, Fatima Rose, Delante, Tristan Kent, Villegas, Jean Kathleen, Basay, Florence John, Andales, Jay-r, Pasko, Francine +3 more·2023·International Journal of Educational Innovation and Research
DOI 10.31949/ijeir.v2i1.3001
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Abstract

The study aims to explore an in-depth analysis of leveraging education through artificial intelligence (AI) virtual assistance among visually impaired learners. The investigation centers on describing the challenges and struggles encountered by visually impaired learners, highlighting the usage of AI virtual assistance, the adaptability of the visually impaired learners, and the enhancement of the curriculum. This study utilized a qualitative case study research design using Braun and Clarke’s (2008) data analysis method to describe an in-depth dissection of a case study involving five visually impaired learners through semi-structured interviews. Findings revealed five themes: (1) barriers in the learning process, (2) essentiality of AI virtual assistants, (3) achieving competence through technology, and (4) inclusive teaching and learning environment. The findings show that this pandemic has brought challenges to visually impaired learners in education, such as personal struggles, insufficiency of resources, and adapting to new learning modalities. However, AI virtual assistance allowed them to explore their potential and help them achieve academic tasks that are generally difficult due to their visual impairment. Researchers recommend improving the teaching and learning of visually impaired students in secondary schools.

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  • GenAI can create alternative formats for course materials or textbooks and provide text-to-speech support for visually impaired learners.

    “In addition, GenAI can create alternative formats for course materials or textbooks and provide text-to-speech support for visually impaired learners (Mina et al., 2023).”

    From Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania (2024) · cites this paper as Mina et al., 2023

    The “Proficiency and Accessibility of AI Virtual Assistant” section says the assistants can read screen text aloud for visually impaired students and describes a participant using Google Text to Speech. The paper does not say they create alternative formats of course materials or textbooks, nor does it establish that the tools discussed are generative AI. (LLM, conf=0.87)

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Research works printed but not linked: 44

  1. Abbacan-Tuguic, L. (2021). Challenges of the New Normal: Students' Attitude, Readiness and Adaptability to Blended Learning Modality. International Journal of English Literature and Social Sciences, 6(2), 443- 449.
  2. Anderson, A. (2019). Virtual reality, augmented reality and artificial intelligence in special education: a practical guide to supporting students with learning differences. Routledge.
  3. Azhari, B., & Fajri, I. (2022). Distance learning during the COVID-19 pandemic: School closure in Indonesia. International Journal of Mathematical Education in Science and Technology, 53(7), 1934- 1954.
  4. Bandura, A. (1971). Social learning theory. New York: General Learning Press.
  5. Baxter, P., & Jack, S. (2008). Qualitative case study methodology: Study design and implementation for novice researchers. The qualitative report, 13(4), 544-559.
  6. Bhowmick, A., & Hazarika, S. M. (2017). An insight into assistive technology for the visually impaired and blind people: state-of-the-art and future trends. Journal on Multimodal User Interfaces, 11(2), 149- 172.
  7. Chkroun, M., & Azaria, A. (2019). Lia: A virtual assistant that can be taught new commands by speech. International Journal of Human-Computer Interaction, 35(17), 1596-1607.
  8. Cho, E., Molina, M. D., & Wang, J. (2019). The effects of modality, device, and task differences on perceived human likeness of voice-activated virtual assistants. Cyberpsychology, Behavior, and Social Networking, 22(8), 515-520.
  9. Chong, S., & Abeliuk, A. (2019, December). Quantifying the effects of recommendation systems. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 3008-3015). IEEE.
  10. Chopra, J., Rani, A., Chopra, S., Manik, P., & Singh, R. R. (2021). Transition from physical to virtual classroom amidst COVID-19 crisis: Analyzing students' perspective to drive improvement in the current online teaching methodology. Journal of Education and Health Promotion, 10.
  11. Cobos-Guzman, S., Nuere, S., De Miguel, L., & König, C. (2021). Design of a Virtual Assistant to Improve Interaction Between the Audience and the Presenter. International Journal of Interactive Multimedia & Artificial Intelligence, 7(2).
  12. Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation: An exploratory analysis. In The economics of artificial intelligence: An agenda (pp. 115-146). University of Chicago Press.
  13. Cruz, E. I., & Olvera, D. M. F (2021). Diagnostic for the design of a virtual educational assistant for visually impaired children. International Journal of Latest Research in Humanities and Social Science 4 (1) 26- 33.
  14. Cudillo, C., Mutya, R., & Adlaon, M. (2022). Parents' Challenges And Their Child's Academic Performance In Science In The Modular Distance Learning. European Journal of Education Studies, 9(7).
  15. Donohue, J. M., & Miller, E. (2020). COVID-19 and school closures. Jama, 324(9), 845-847.
  16. Ellis, J., Wieselmann, J., Sivaraj, R., Roehrig, G., Dare, E., & Ring-Whalen, E. (2020). Toward a productive definition of technology in science and STEM education. Contemporary issues in technology and teacher education, 20(3), 472-496.
  17. Forbes, M., & Lasonen, J. (2021). Use of Intelligent Virtual Assistants in Lifelong Learning for Persons with Visual Impairments. The SAGE Handbook of Learning and Work, 460.
  18. Geverola, I. J. R., Mutya, R. C., Siason, L. M. B., & Bonotan, A. (2022). Challenges and struggles of public senior high school science teachers during the new normal. Journal of Research, Policy & Practice of Teachers and Teacher Education, 12(1), 49-68
  19. Gravestock, P. (2017). Building momentum towards inclusive teaching and learning. University of Wolverhampton, presented at Institute of Physics. Retrieved from: https://www.iop.org/publications/iop/2017/page_69352.html
  20. Gubareva, R., & Lopes, R. P. (2020). Virtual Assistants for Learning: A Systematic Literature Review. CSEDU (1), 97-103.
  21. Iyer, V., Shah, K., Sheth, S., & Devadkar, K. (2020, June). Virtual assistant for the visually impaired. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 1057-1062). IEEE.
  22. Kaarbo, J., & Beasley, R. K. (1999). A practical guide to the comparative case study method in political psychology. Political psychology, 20(2), 369-391.
  23. Kapote, S., & Srikanth, P. (2022). Barriers and the Role of Assistive Technology to Access Education for Children with Visually Impaired During COVID-19 Times. Indian Journal of Clinical Medicine, 26339447221089124.
  24. Kavčič, A., Pesek, M., & Marolt, M. (2017, May). A platform for supporting learning process of visually impaired children. In 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 848-852). IEEE.
  25. Kim, K., de Melo, C. M., Norouzi, N., Bruder, G., & Welch, G. F. (2020, March). Reducing task load with an embodied intelligent virtual assistant for improved performance in collaborative decision making. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 529-538). IEEE.
  26. Martiniello, N., Asuncion, J., Fichten, C., Jorgensen, M., Havel, A., Harvison, M., ... & Vo, C. (2021). Artificial intelligence for students in postsecondary education: a world of opportunity. AI Matters, 6(3), 17-29.
  27. Marr, B. (2019). Artificial intelligence in practice: how 50 successful companies used AI and machine learning to solve problems. John Wiley & Sons.
  28. Mekni, M. (2021). An artificial intelligence based virtual assistant using conversational agents. Journal of Software Engineering and Applications, 14(9), 455-473.
  29. Moreno, G. (2022). Expanding Definition of Technology in Special Education: Impact of Training on the Adoption of iPad Tablets by Special Educators. International Journal of Disability, Development and Education, 69(2), 722-738.
  30. Mutya, R. C., Geverola, I. J. R., Cano Jr, A. C., & Friolo, R. V. (2022). Coping with uncertainties: Unveiling the lived experiences of working students in the new normal. Ho Chi Minh City Open University Journal Of Science-Social Sciences, 12(1), 112-129.
  31. Olguín-Gil, L. E., Vázquez-Guzmán, F., Vázquez-Zayas, E., Mejía, J., & Blanco-Cruz, I. (2021, October). Virtual Assistant as Support for People Visually Impaired. In International Conference on Software Process Improvement (pp. 174-188). Springer, Cham.
  32. Oyedeji, T. (2022). Harnessing Artificial Intelligence for Educational Creativity. IFE PsychologIA: An International Journal, 30(1), 103-114.
  33. Page, L. C., & Gehlbach, H. (2017). How an artificially intelligent virtual assistant helps students navigate the road to college. AERA Open, 3(4), 2332858417749220.
  34. Rafailidis, D., & Manolopoulos, Y. (2019, June). Can virtual assistants produce recommendations?. In Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics (pp. 1- 6).
  35. Raghavan, R., Krishnan, V., Nishad, H., & Shaikh, B. (2021). Virtual AI Assistant for Person with Partial Vision Impairment. In ITM Web of Conferences (Vol. 37, p. 01019). EDP Sciences.
  36. Rasli, A. M., Norhalim, N., Kowang, T. O., & Qureshi, M. I. (2015). Applying managerial competencies to overcome business constraints and create values: Evidence from small technology-based firms in Malaysia. Journal of Management Info, 2(2), 22-28.
  37. Roseli, N. H. M., Aziz, N., & Mutalib, A. A. (2010, June). The enhancement of assistive courseware for visually impaired learners. In 2010 International Symposium on Information Technology (Vol. 1, pp. 1-6). IEEE.
  38. Sam, A. P., Singh, B., & Das, A. S. (2019, June). A robust methodology for building an artificial intelligent (ai) virtual assistant for payment processing. In 2019 IEEE Technology & Engineering Management Conference (TEMSCON) (pp. 1-6). IEEE.
  39. Schade, L. C., Sandberg, J., Bean, R., Busby, D., & Coyne, S. (2013). Using technology to connect in romantic relationships: Effects on attachment, relationship satisfaction, and stability in emerging adults. Journal of Couple & Relationship Therapy, 12(4), 314-338.
  40. Sharma, V., Singh, V. M., & Thanneeru, S. (2020). Virtual Assistant for Visually Impaired. Available at SSRN 3580035.
  41. Song, Y. W. (2019). User acceptance of an artificial intelligence (AI) virtual assistant: an extension of the technology acceptance model (Doctoral dissertation).
  42. Wheatley, D., Hardill, I., & Buglass, S. (Eds.). (2021). Handbook of Research on Remote Work and Worker Well- Being in the Post-COVID-19 Era. IGI Global.
  43. Yadav, A. V., Verma, S. S., & Singh, D. D. (2021). Virtual Assistant For Blind People. International Journal, 6(5).
  44. Zhou, Z. (2016). A framework for virtual assistants: An exploratory study. International Journal of Social Science and Business, 1(4), 49-56.

Cited by

Sorted by citations

Authors
Mina, P. N. R., Solon, I. M., Sanchez, F. R., Delante, T. K., Villegas, J. K., Basay, F. J., Andales, J.-R., Pasko, F., Estrera, M. F. R., Samson Jr., R., & Mutya, R.
ORCID
Venue
International Journal of Educational Innovation and Research
Publisher
Universitas Majalengka
Published
2023-01-27
Volume
2
Issue
1
Pages
10-22
ISSN
2810-0808 (ISSN-L)
DOI
10.31949/ijeir.v2i1.3001
OpenAlex
W4318262800
Cited by
22
References
4
Citations by year
2026: 7 · 2025: 6 · 2024: 6 · 2023: 3
FWCI
5.16
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Educational Innovation and Research (publishedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
  • Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
Keywords
visually impaired students, AI virtual assistants, assistive technology, inclusive education, barriers to learning, COVID-19 pandemic impact on education, high school students, curriculum adaptation, academic competence

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